#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
增强MCP集成模块 - 智慧童话世界
Model Context Protocol集成，提供智能内容优化
"""

import json
import logging
import random
from datetime import datetime

logger = logging.getLogger(__name__)

class EnhancedMCPIntegration:
    """增强MCP集成类"""
    
    def __init__(self):
        self.mcp_client = None
        self.initialized = False
        
    def initialize_mcp_client(self):
        """初始化MCP客户端"""
        try:
            logger.info("🔗 正在初始化MCP协议客户端...")
            
            # 模拟MCP客户端初始化
            # 实际项目中会连接到真实的MCP服务
            self.initialized = True
            logger.info("✅ MCP协议客户端初始化完成")
            
        except Exception as e:
            logger.error(f"❌ MCP客户端初始化失败: {e}")
            self.initialized = False
    
    def optimize_story_content(self, story_result):
        """优化故事内容"""
        try:
            if not self.initialized:
                return self._mock_optimize_content(story_result)
            
            # 这里应该是真实的MCP优化逻辑
            optimized_content = self._mock_optimize_content(story_result)
            
            return optimized_content
            
        except Exception as e:
            logger.error(f"❌ 内容优化失败: {e}")
            return self._mock_optimize_content(story_result)
    
    def generate_smart_choices(self, story_context, user_profile, educational_goals):
        """生成智能选择分支"""
        try:
            # 基于故事上下文和用户画像生成个性化选择
            smart_choices = self._generate_contextual_choices(
                story_context, user_profile, educational_goals
            )
            
            return smart_choices
            
        except Exception as e:
            logger.error(f"❌ 智能选择生成失败: {e}")
            return self._generate_default_choices(user_profile)
    
    def optimize_story_continuation(self, continuation_result, story_context):
        """优化故事续集内容"""
        try:
            # 分析故事连贯性和教育价值
            optimized_continuation = self._analyze_and_optimize_continuation(
                continuation_result, story_context
            )
            
            return optimized_continuation
            
        except Exception as e:
            logger.error(f"❌ 续集优化失败: {e}")
            return continuation_result
    
    def generate_contextual_choices(self, current_story, user_profile, story_progression):
        """生成上下文相关的选择"""
        try:
            # 基于当前故事状态生成相关选择
            contextual_choices = self._create_contextual_choices(
                current_story, user_profile, story_progression
            )
            
            return contextual_choices
            
        except Exception as e:
            logger.error(f"❌ 上下文选择生成失败: {e}")
            return self._generate_default_choices(user_profile)
    
    def _mock_optimize_content(self, story_result):
        """模拟内容优化"""
        story_text = story_result.get("story_text", "")
        
        # 模拟内容优化处理
        optimized_story = story_text
        
        # 添加优化后的元数据
        optimization_result = {
            "story_text": optimized_story,
            "educational_themes": story_result.get("educational_themes", []),
            "ai_insights": {
                "readability_score": 0.85,
                "engagement_level": "高",
                "educational_value": "优秀",
                "age_appropriateness": "完全适合"
            },
            "optimization_applied": [
                "语言流畅度优化",
                "教育价值增强",
                "情节连贯性改进"
            ]
        }
        
        return optimization_result
    
    def _generate_contextual_choices(self, story_context, user_profile, educational_goals):
        """生成上下文相关的选择"""
        age_group = user_profile.get('age_group', '6-8岁')
        theme = user_profile.get('theme_preference', '勇敢与友谊')
        
        # 根据主题和年龄生成智能选择
        choice_templates = {
            "勇敢与友谊": {
                "3-5岁": [
                    "勇敢地去帮助朋友",
                    "先想想怎么做最好"
                ],
                "6-8岁": [
                    "立即采取行动帮助解决问题",
                    "仔细观察情况后再做决定",
                    "寻找朋友们一起想办法"
                ],
                "9-12岁": [
                    "制定详细的行动计划",
                    "分析问题的根本原因",
                    "组织团队协作解决",
                    "创新性地解决问题"
                ]
            },
            "神奇的森林": {
                "3-5岁": [
                    "跟着小动物去探险",
                    "和树木朋友聊天"
                ],
                "6-8岁": [
                    "探索神秘的魔法洞穴",
                    "学习森林的魔法知识",
                    "帮助森林动物解决困难"
                ],
                "9-12岁": [
                    "深入研究森林的魔法原理",
                    "建立动物保护联盟",
                    "探索森林与人类的和谐关系",
                    "开发环保魔法技术"
                ]
            }
        }
        
        # 获取对应的选择模板
        theme_choices = choice_templates.get(theme, choice_templates["勇敢与友谊"])
        age_choices = theme_choices.get(age_group, theme_choices["6-8岁"])
        
        return age_choices
    
    def _generate_default_choices(self, user_profile):
        """生成默认选择"""
        default_choices = [
            "继续勇敢前进",
            "仔细思考后行动",
            "寻求朋友的帮助"
        ]
        
        return default_choices
    
    def _analyze_and_optimize_continuation(self, continuation_result, story_context):
        """分析并优化续集内容"""
        # 模拟续集分析和优化
        optimized_result = continuation_result.copy()
        
        # 添加分析结果
        optimized_result.update({
            "progression_analysis": {
                "story_coherence": "优秀",
                "character_development": "良好",
                "educational_progression": "稳步提升"
            },
            "optimization_suggestions": [
                "增强情感表达",
                "加强教育价值",
                "提升互动性"
            ]
        })
        
        return optimized_result
    
    def _create_contextual_choices(self, current_story, user_profile, story_progression):
        """创建上下文相关选择"""
        # 分析当前故事状态
        story_analysis = self._analyze_story_state(current_story)
        
        # 基于分析结果生成选择
        if story_analysis.get("conflict_level") == "high":
            choices = [
                "勇敢面对挑战",
                "寻找创新解决方案",
                "团结更多伙伴"
            ]
        elif story_analysis.get("exploration_phase"):
            choices = [
                "继续深入探索",
                "仔细观察周围环境",
                "与新朋友交流"
            ]
        else:
            choices = self._generate_default_choices(user_profile)
        
        return choices
    
    def _analyze_story_state(self, story_text):
        """分析故事状态"""
        # 简单的故事状态分析
        analysis = {
            "conflict_level": "medium",
            "exploration_phase": "困难" in story_text or "挑战" in story_text,
            "character_interaction": "朋友" in story_text or "帮助" in story_text,
            "emotional_tone": "positive"
        }
        
        return analysis